Dynamic Pricing Scheme Research Articles

Power distribution network design considering the distributed generations and differential and dynamic pricing

In this study, a power distribution network design model is developed considering voltage control, as well as differential and dynamic pricing schemes. The objective is to maximize power distribution network profits under uncertain demands and parameters, such as power generation capacity, investment and generation cost of renewable distributed generation units, and the penalty budget for unsatisfied demands. The model determines the percentage of the power supply to the customer groups, the amount of power supplied to each customer group, the technology and capacity of renewable distributed generation units, and the prices offered. The proposed pricing scheme is differential for different types of customers with allowable voltage limits. The price is dynamic according to the planning time, including low, medium, and peak times. A fuzzy stochastic programming approach is developed to solve this problem. By simultaneously using triangular crisp numbers and probabilistic variables, the fuzzy stochastic programming integrates the fuzzy and stochastic approaches to deal with the uncertainty of demand and parameters. The numerical analysis shows that, as the confidence level decreases, the capacity of renewable distributed generation units and the profit increase. The results also show that when the decision maker responds to the uncertainty in demand with a high confidence level, the optimum profit can be obtained; approximately 55.90% of the power is supplied to low-demand customers.

ReLeDP: Reinforcement-Learning-Assisted Dynamic Pricing for Wireless Smart Grid

The smart grid must ensure that power providers can obtain substantial benefits by selling energy, while at the same time, they need to consider the cost of consumers. To realize this win-win situation, the smart grid relies on dynamic pricing mechanisms. However, most of the existing dynamic pricing schemes are based on artificial objective rules or conventional models, which cannot ensure the desired effectiveness. Thus, we apply reinforcement learning to model the supply-demand relationship between power providers and consumers in a smart grid. The dynamic pricing problem of the smart grid is modeled as a discrete Markov decision process, and the decision process is solved by Q-learning. Now, the success of any intelligent dynamic pricing scheme relies on timely data transmission. However, the scale and speed of data generation can create several network bottlenecks that can further reduce the performance of any dynamic pricing scheme. Hence, to overcome this challenge, we have proposed an artificial-intelligence-based adaptive network architecture that adopts software-defined networking. In this architecture, we have used a self-organized map-based traffic classification approach followed by a dynamic virtual network embedding mechanism. We demonstrate the effectiveness of the dynamic pricing strategy supported through adaptive network architecture based on various performance indicators. The outcomes suggest that the proposed strategy is of great significance to realize the sustainability of power energy in the future. Lastly, we discuss various implementation challenges and future directions before concluding the article.

A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.

Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes.

Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion. Dynamic toll pricing schemes effectively achieve reliable travel time on ELs. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data collected by sensors installed along the ELs. Thus, the operation of dynamic pricing critically depends on the accuracy of data collected by such traffic sensors. However, no previous research has been conducted to explicitly investigate the impact of sensor failures and erroneous sensors’ data on toll computations. This research fills this gap by examining the effects of sensor failure and faulty detection scenarios on ELs tolls calculated by a dynamic pricing algorithm. The paper’s methodology relies on applying the dynamic toll pricing algorithm implemented in the field and utilizing the fundamental speed-volume relationship to ‘simulate’ the sensors’ reported data. We implemented the methodology in a case study of ELs on Interstate-95 in Southeast Florida. The results have shown that the tolls increase when sensors erroneously report higher than actual traffic demand. Moreover, it has been found that the accuracy of individual sensors and the number of sensors utilized to estimate traffic conditions are critical for accurate toll calculations.

Economics of the Li-ion batteries and reversible fuel cells as energy storage systems when coupled with dynamic electricity pricing schemes

The intermittent nature of power generated by renewable energy systems makes it harder for many power grids to accommodate large generations in short period of times. In this paper, we quantify and discuss the cost associated with storing excess energy from the wholesale electricity markets in the United States in the form of hydrogen using proton exchange membrane reversible fuel cells (PEM-RFC) and in the form of electrochemical energy stored in lithium-ion batteries (LIB). The key financial metric used in this study is the levelized cost of electricity storage (LCOS). Results show that electricity can be stored in many regions in the U.S. at very competitive costs, reaching as low as 16.6¢/kWh using RFC and 8.6¢/kWh using LIB using electricity purchased from California Independent System Operator (CAISO). These values are near the future targets set by the U.S. Department of Energy of 5¢/kWh. Sensitivity analysis revealed that the LCOS using both ESSs is dependent on the following design and financial parameters from higher to lower effect: ESS roundtrip efficiency, system capital cost and the expected system lifetime, respectively. Analysis of system performance and economics showed that LIB seem to have better economic advantage based on the LCOS values, but lack the flexibility of the RFC system. RFC, on the other hand, has more advantages in terms of the size of energy storage capacity, operational charging/discharging flexibility, and the effect on increasing the grid resiliency, especially during long periods of power outages.

Dynamic Trust Enforcing Pricing Scheme for Sensors-as-a-Service in Sensor-Cloud Infrastructure

In this paper, the problem of provisioning high quality of Sensors-as-a-Service (Se-aaS) in the presence of competitive sensor-owners, i.e., oligopolistic market, and heterogeneous sensor nodes in service-oriented sensor-cloud is studied. Oligopolistic sensor-owners adopt unfair means to degrade the quality of service provided by other sensor-owners in the sensor-cloud market. In order to address this problem, a dynamic pricing scheme, named DETER, is proposed in this work to enforce trust among the sensor-owners for maintaining the quality of Se-aaS provided by the Sensor-Cloud Service Provider (SCSP). Each sensor node calculates distributed trust opinion for other nodes, while the SCSP calculates centralized trust opinion for each sensor-owner. A Single-Leader-Multiple-Follower Stackelberg Game is formulated in which the SCSP acts as the leader and decides price to be paid to each sensor-owner, while ensuring maximum profit. On the other hand, the sensor-owners act as the followers and decide their strategies for earning maximum profit. Thereby, using DETER, SCSP enforces high trust among the sensor-owners. Additionally, using DETER, energy consumption of sensor nodes in sensor-cloud decreases by 4.69-11.56 percent, and network overhead decreases by 52.6-56.53 percent. The trade-off between price earned by the sensor-owners and profit of the SCSP in service-oriented sensor-cloud is also maintained using DETER.

Energy Trading among Power Grid and Renewable Energy Sources: A Dynamic Pricing and Demand Scheme for Profit Maximization.

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid.

An Application of a Deep Q-Network Based Dynamic Fare Bidding System to Improve the Use of Taxi Services during Off-Peak Hours in Seoul

The problem of structural imbalance in terms of supply and demand due to changes in traffic patterns by time zone has been continuously raised in the mobility market. In Korea, unlike large overseas cities, the waiting time tolerance increases during the daytime when supply far exceeds demand, resulting in a large loss of operating profit. The purpose of this study is to increase taxi demand and further improve driver’s profits through real-time fare discounts during off-peak daytime hours in Seoul, Korea. To this end, we propose a real-time fare bidding system among taxi drivers based on a dynamic pricing scheme and simulate the appropriate fare discount level for each regional time zone. The driver-to-driver fare competition system consists of simulating fare competition based on the multi-agent Deep Q-Network method after developing a fare discount index that reflects the supply and demand level of each region in 25 districts in Seoul. According to the optimal fare discount level analysis in the off-peak hours, the lower the OI Index, which means the level of demand relative to supply, the higher the fare discount rate. In addition, an analysis of drivers’ profits and matching rates according to the distance between the origin and destination of each region showed up to 89% and 65% of drivers who actively offered discounts on fares. The results of this study in the future can serve as the foundation of a fare adjustment system for varying demand and supply situations in the Korean mobility market.

Intelligent Dynamic Pricing Scheme for Demand Response in Brazil Considering the Integration of Renewable Energy Sources

This paper proposes a novel dynamic pricing scheme for demand response with individualized tariffs by consumption profile, aiming to benefit both customers and utility. The proposed method is based on the genetic algorithm, and a novel operator called mutagenic agent is proposed to improve algorithm performance. The demand response model is set by using price elasticity theory, and simulations are conducted based on elasticity, demand, and photovoltaic generation data from Brazil. Results are evaluated considering the integration effects of renewable energy sources and compared with other two pricing strategies currently adopted by Brazilian utilities: flat tariff and time-of-use tariff. Simulation results show the proposed dynamic tariff brings benefits to both utilities and consumers. It reduces the peak load and average cost of electricity and increases utility profit and load factor without the undesirable rebound effect.

An energy-efficient incentive mechanism for resource recycling in multi-tenant datacenters

Multi-tenant public cloud is an important sector of cloud computing. While it has witnessed an unprecedented proliferation in the past years, its low resource utilization that incurs enormous energy waste has received great attention. Since the resource of multi-tenant datacenters is typically rented to the tenants in a reserved manner, the cloud provider is unable to reduce the energy consumption or improve resource utilization via turning down idle servers. To address this challenge, this paper proposes an efficient incentive mechanism to stimulate the recycling of reserved idle resources. Specifically, by economically compensating the tenants, the reserved resources that are idle can be recycled by the cloud providers to host other cloud applications, such as e-commerce and search engine. Taking advantage of the Lyapunov optimization method, this paper rigorously designs and analyzes a dynamic pricing scheme for the recycled reserved resources. Without requiring the future information as a priori, the proposed pricing scheme can make online decisions to optimize the long-term operational cost of the cloud provider, while still maintaining the stability of the system. Both rigorous theoretical analysis and extensive trace-driven simulations verify the efficacy of the proposed incentive mechanism.

Flat-Rate Pricing and Truthful Offloading Mechanism in Multi-Layer Edge Computing

Mobile Edge Computing (MEC) is a promising paradigm to ease the computation burden of Internet-of-Things (IoT) devices by leveraging computing capabilities at the network edge. With the yearning needs for resource provision from IoT devices, the queueing delay at the edge nodes not only poses a colossal impediment to achieving satisfactory quality of experience (QoE) for the IoT devices but also to the benefits of the edge nodes owing to escalating energy expenditure. Moreover, since the service providers may differ, computationally competent entities' computing services should entail economic compensation for the incurred energy expenditure and the capital investment. Therefore, the workload allocation mechanism, where we consider flat-rate and dynamic pricing schemes in the multi-layer edge computing structure, is much-needed. We use Stackelberg game to capture the inherent hierarchy and interdependence between the second-layer edge node (SLEN) and first-layer edge nodes (FLENs). A truthful admission control mechanism grounded on the optimal workload allocation is designed for FLENs without violating end-to-end (E2E) latency requirements. We prove that a Stackelberg equilibrium with the E2E latency guarantee and truthfulness exists and can be reached through proposed algorithm. Simulation results confirm the effectiveness of our scheme and illustrate several insights.

A fluid approximation for the single-leg fare allocation problem with nonhomogeneous poisson demand

Fare allocation for legs and O&D pairs plays a crucial role in airline revenue management. Despite a large number of dynamic pricing studies, there are only a few widely adopted studies in which assumptions affect most tactical decisions with potentially large impacts on airline profitability. These decisions involve approximating future pricing schemes, allocation of fare classes, and setting booking limits. We propose a fare allocation model for a single leg in the presence of a realistic nonhomogeneous Poisson demand with an increasing rate. We aim to compute when and how to markup the price for an airfare product (switch to an upper fare class) to maximize the expected revenue. We study a fluid approximation of the underlying stochastic problem considering independent demand from each customer segment and examine different properties that lead to several important insights. Finally, we propose a dynamic look-ahead pricing scheme to compare our fluid approximation results against the well-known EMSRb heuristic and a dynamic programming solution on randomly generated booking request data. Numerical examples illustrate the effectiveness of our proposed approach.

Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.

Impact of distribution tariffs on prosumer demand response

Distributed energy resources (DERs) may enable prosumers to deliver demand response under dynamic energy pricing schemes. Facing wholesale market prices, the DER scheduling of a profit-maximising prosumer minimises the total system energy cost as well. However, regulated electricity bill components, i.a., distribution tariffs for recovering grid costs, may distort these price signals. To study the impact of distribution tariff structures on DER scheduling, we develop a short-term linear optimisation model that determines the cost-optimal DER schedule based on wholesale electricity prices and a distribution tariff. We compare five distribution tariff structures for four combinations of PV, batteries, and heat pumps, and define two metrics to characterise the results: (i) the novel ‘relative flexibility value’, which quantifies energy cost inefficiencies, and (ii) ‘rate of self-consumption’. In a case study, we show how prosumers make trade-offs between energy and distribution costs, depending on the distribution tariff structure. We describe the mechanisms through which different distribution tariff structures alter DER operations, and quantify the tariffs' impact on the system energy cost. Generally, we find that tariff types which stimulate self-consumption increase the system energy cost and vice versa. • Distribution tariffs distort the scheduling of distributed energy resources. • Prosumers trade off energy cost increases with distribution cost decreases. • Self-consumption can reduce grid interaction and related private costs. • Tariffs with high self-consumption perform low on cost-efficiency and vice versa.

A dynamic pricing scheme for electric vehicle in photovoltaic charging station based on Stackelberg game considering user satisfaction

Electric vehicle (EV) has increasingly become an important part of the load of smart power grid in recent years. Since uncoordinated EV would stress the distributed grid system, the establishment of Photovoltaic (PV) charging station is of great significance to optimizing domestic power demand and developing a low-carbon society. In this paper, a dynamic pricing scheme based on Stackelberg game for EV charging station with PV system is proposed. Considering the uncertainty of electricity changes before and after charging, we divide one day into two kinds of time intervals: charging time interval and pricing time interval, and the demand response (DR) based on preference of EV user is also implemented. Moreover, the constraint reflecting the fluctuation (uncertainty) of power consumption caused by the charging continuity of EV is also introduced to the model. By analyzing the probability property of constraints, the game model with uncertainty is equivalently transformed into a convex game at the end. Additionally, the existence and uniqueness of Stackelberg Equilibrium (SE) is proved, so the optimal strategies of all participants are guaranteed. Finally, a distributed algorithm is designed to obtain the SE. The simulation results show that the pricing scheme proposed in this paper can better reduce the selling price of charging station and increase the profit of charging station.

Inducing flexibility of household electricity demand: The overlooked costs of reacting to dynamic incentives

Flexible electricity demand through dynamic pricing11We use the term ‘dynamic pricing’ to describe electricity tariffs that are continually adjusted to (partially) reflect the current price at the spot market or at the day ahead market such as real-time pricing. Time of use pricing is not covered by our use of the term because time of use tariffs are pre-declared and not continually adjusted to reflect current market conditions. Note that the term dynamic pricing is sometimes defined more broadly e.g. as any tariff structure different from a flat tariff. is becoming more critical for net-balancing as the share of intermittent wind and solar electricity production increases. However, people are prone to habits and regularity, so this kind of dynamic pricing schemes may also impose a greater cost of reacting to price incentives. In a randomized field experiment we compare the household consumer welfare effects of reacting to dynamic electricity price incentives to those of reacting to time of use incentives. We utilized smart-metered hourly electricity consumption data to unobtrusively measure treatment effects. We found that a dynamic incentive of 1 DKK/kWh induced an average change in electricity consumption per household of 0.182 kWh during the 3-hour target period. The corresponding time of use incentive induced an average change in electricity consumption of 0.351 kWh. This implies that dynamic incentives reduce consumer surplus from reacting by half, compared to reacting to corresponding time of use incentives and therefore that households derive a substantial welfare benefit from the regularity and predictability provided by time of use pricing. This suggests that the tariff structure that optimally balances demand flexibility and the associated consumer welfare costs is likely a combination of time of use and dynamic pricing incentives.

Cost-Efficient Scheduling of Multicast Transfers with Deadline Guarantees Across Edge Datacenters

Moderate-scale datacenters are increasingly deployed at the network edge to support low-latency and high-bandwidth internet of Things (IoT) and 5G applications. These applications usually have bulk data to transfer from one datacenter to several datacenters, which can cause a significant amount of bandwidth costs. Traffic engineering (TE) systems at the network edge must minimize the bandwidth costs of multicast transfers across edge datacenters. To avoid service quality degradation, TE should also guarantee transfer deadlines, which have received little attention in existing work. Due to the dynamic bandwidth pricing schemes, it is challenging to strike a good balance between minimizing costs and guaranteeing transfer deadlines. In this paper, we present CDScheduler a cost-efficient scheduling solution for multicast transfers with deadline guarantees. To reduce bandwidth costs, CDScheduler uses Steiner trees for forwarding and adaptive routing that considers bandwidth price variation and transfer demands. We formulate the cost-efficient multicast transfer scheduling problem and propose an algorithm based on linear program relaxation and randomized rounding to find a solution that guarantees timely completion and reduces cost. Extensive evaluations show that CDScheduler can reduce bandwidth cost significantly and outperforms the state-of-the-art solutions by cutting down up to 78% bandwidth cost.

Q-Safe: QoS-Aware Pricing Scheme for Provisioning Safety-as-a-Service

In this paper, we propose a Quality of Service (QoS)-aware pricing scheme, termed as Q-Safe, for provisioning safety-related decisions to the end-users. A Safe-aaS platform provides customized decisions to the end-users, as per their requirement. In this proposed pricing scheme, we consider the presence of multiple Safety Service Providers (SSPs) in the Safe-aaS platform. Therefore, the end-users possess the opportunity to select a SSP, depending on the price charged by them. The end-users may compromise with the quality of the decision provided through the selection of the available safety services at a low cost. Considering road transportation as the application scenario of Safe-aaS and to address these above-mentioned issues, we propose a dynamic pricing scheme, Q-Safe. We introduce the concept of varying price to be charged by the SSPs for each of the decision parameters, based on the fluctuation in the value of these parameters with time. Each of the end-users selects certain decision parameters, among the ones displayed in the Web portal. Thereafter, the SSPs suggest decision parameters to the end-users depending upon their present geographical location. To model these interactions between the SSPs and the end-users, we map the scenario with Non-Cooperative Multiple Leader Multiple Follower Stackelberg game.

A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network

This study summarizes a critical review on EVs’ optimal charging and scheduling under dynamic pricing schemes. A detailed comparison of these schemes, namely, Real Time Pricing (RTP), Time of Use (ToU), Critical Peak Pricing (CPP), and Peak Time Rebates (PTR), is presented. Globally, the intention is to reduce the carbon emissions (CO2) has motivated the extensive practice of Electric Vehicles (EVs). The uncoordinated charging and uncontrolled integration however of EVs to the distribution network deteriorates the system performance in terms of power quality issues. Therefore, the EVs’ charging activity can be coordinated by dynamic electricity pricing, which can influence the charging activities of the EVs customers by offering flexible pricing at different demands. Recently, with developments in technology and control schemes, the RTP scheme offers more promise compared to the other types of tariff because of the greater flexibility for EVs’ customers to adjust their demands. It however involves higher degree of billing instability, which may influence the customer’s confidence. In addition, the RTP scheme needs a robust intelligent automation system to improve the customer’s feedback to time varying prices. In addition, the review covers the main optimization methods employed in a dynamic pricing environment to achieve objectives such as power loss and electricity cost minimization, peak load reduction, voltage regulation, distribution infrastructure overloading minimization, etc.

A discrete-time second-best dynamic road pricing scheme considering the existence of multiple equilibria

When multiple equilibria exist, the desired traffic equilibrium, may not be reached through a day-to-day dynamic adjustment process. Han et al. [“Discrete-Time Day-to-Day Dynamic Congestion Pricing Scheme Considering Multiple Equilibria.” Transportation Research Part B: Methodological 104: 1–16.] proposed a day-to-day dynamic pricing scheme charged on all links to ensure the desired traffic equilibrium can be achieved even when multiple equilibria exist. However, due to many practical constraints, the second-best pricing scheme has more practical interests. This study first discussed the issues of existing congestion pricing schemes for second-best case. Then, this study developed a second-best dynamic road pricing scheme that is implemented on a dynamic subset of the road links in the network. Our rigorous theoretical proof and numerical tests prove that the dynamic pricing scheme can drive the traffic state evolution towards the desired second-best traffic user equilibrium state from any initial traffic state.